Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2021 / Cilt: 26 Sayı: 3

Restoration of Clipped Audio Signals

KIRPILMIŞ SES İŞARETLERİNİN YENİLENMESİ

Mehmet Erdal Özbek(İzmir Katip Çelebi Üniversitesi Mühendislik ve Mimarlık Fakültesi)0000-0001-5840-7960
Pages
1035–1046
Publication Date
2021-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It has been shown that the proposed method achieves better signal to noise ratio performance compared to the other methods in cases of higher clipping ratios.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Restoration process is performed to remove degradations formed on the audio signals.
📊
Key Findings & Data:In order to evaluate the performance of the proposed method, experiments were performed on various speech and music signal examples.
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Conclusion & Impact:It has been shown that the proposed method achieves better signal to noise ratio performance compared to the other methods in cases of higher clipping ratios.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Ses yenileme · Kırpılma · Seyrek gösterim

Abstract

Restoration process is performed to remove degradations formed on the audio signals. In the restoration of clipped audio signals, which is one of these degradations, the degraded section is aimed to be restored to its original by the part of the undegraded section of the signal. The transformation of the signal from as normally given or recorded in the time domain to a different domain and thus reducing the number of samples required to be represented might be possible due to sparse representation. In this study, a restoration method is presented that relies on sparse representation of the discrete Fourier transform coefficients of the signal. In order to evaluate the performance of the proposed method, experiments were performed on various speech and music signal examples. It has been shown that the proposed method achieves better signal to noise ratio performance compared to the other methods in cases of higher clipping ratios.

Özet

Ses işaretlerinde oluşan bozulmaların ortadan kaldırılması için yenileme işlemi yapılmaktadır. Bu bozulmalardan birisi olan kırpılmış ses işaretlerinin yenileme işleminde, işaretin bozulmamış bölgesindeki işaret parçası aracılığı ile işaretin bozulmaya uğramış bölgesinin özgün durumuna geri getirilmesi amaçlanmaktadır. İşaretin normal olarak verildiği ya da kayıt edildiği zaman ortamından farklı bir ortama dönüştürülmesi ve bu sayede temsil edilmesi için gerekli örnek sayısının azalması seyrek gösterim sayesinde mümkün olmaktadır. Bu çalışmada işaretin ayrık Fourier dönüşümü katsayılarının oluşturduğu seyrek gösterime dayanan bir yenileme yöntemi sunulmaktadır. Önerilen yöntemin başarımının değerlendirilmesi için farklı konuşma ve müzik işaretlerinden oluşan örnekler üzerinde çalışmalar yapılmıştır. Önerilen yöntemin işaretin daha yüksek oranda kırpılması durumunda karşılaştırılan diğer yöntemlere göre daha iyi işaret gürültü oranı başarımı elde ettiği gösterilmiştir.

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Cite This Article

Özbek, M. E. (2021). Restoration of Clipped Audio Signals. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 26 Sayı: 3, 1035–1046. https://doi.org/10.17482/uumfd.912949

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References (22)

  1. Abel, J. S. and Smith, J. O. (1991) Restoring a clipped signal, IEEE International Conference on Acoustics, Speech, and Signal Processing, Toronto, 1745-1748. doi: 10.1109/ICASSP.1991.150655
  2. Adler, A., Emiya, V., Jafari, M. G., Elad, M., Gribonval, R. and Plumbley, M. D. (2012) Audio inpainting, IEEE Transactions on Audio, Speech, and Language Processing, 20(3), 922-932. doi: 10.1109/TASL.2011.2168211
  3. Candes, E. J. (2006) Compressive sampling, International Congress of Mathematicians, Madrid, 1433-1452. doi: 10.4171/022-3/69
  4. Chen, S., Donoho, D. and Saunders, M. (1998) Atomic decomposition by basis pursuit, SIAM Journal on Scientific Computing, 20(1), 33-61. doi: 10.1137/S1064827596304010
  5. Davis, G., Mallat, S. and Avellaneda, M. (1997) Adaptive greedy approximations, Journal of Constructive Approximation, 13, 57-98. doi: 10.1007/BF02678430
  6. Defraene, B., Mansour, N., De Hertogh, S., van Waterschoot, T., Diehl, M. and Moonen, M. (2013) Declipping of audio signals using perceptual compressed sensing, IEEE Transactions on Audio, Speech, and Language Processing, 21(12), 2627-2637. doi: 10.1109/TASL.2013.2281570
  7. Deruty, E. and Tardieu, D. (2014) About dynamic processing in mainstream music, Journal of Audio Engineering Society, 62(1/2), 42-55. doi: 10.17743/jaes.2014.0001
  8. Donoho, D. (2006) Compressed sensing, IEEE Transactions on Information Theory, 52(4), 1289-1306. doi: 10.1109/TIT.2006.871582
  9. Gaultier, C., Kitić, S., Gribonval, R. and Bertin, N. (2021) Sparsity-based audio declipping methods: Selected overview, new algorithms, and large-scale evaluation, IEEE/ACM Transactions on Audio, Speech and Language Processing, 29, 1174-1187. doi: 10.1109/TASLP.2021.3059264
  10. Godsill, S. J. and Rayner, P. J. W. (1998) Digital Audio Restoration - A Statistical Model-Based Approach. London: Springer-Verlag. doi: 10.1007/978-1-4471-1561-8
  11. Janssen, A. J. E. M., Veldhuis, R. N. J. and Vries, L. B. (1986) Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes, IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(2), 317-330. doi: 10.1109/TASSP.1986.1164824
  12. Kitić S., Bertin N. and Gribonval R. (2015) Sparsity and cosparsity for audio declipping: A flexible non-convex approach. in: Vincent E., Yeredor A., Koldovský Z., Tichavský P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science, 9237, 243-250. doi: 10.1007/978-3-319- 22482-4_28
  13. Mallat, S. and Zhang, Z. (1993) Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, 41(12), 3397-3415. doi: 10.1109/78.258082
  14. Mokrý, O. and Rajmic, P. (2020) Audio inpainting: Revisited and reweighted, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2906-2918. doi: 10.1109/TASLP.2020.3030486
  15. Mokrý, O., Záviška, P., Rajmic, P. and Veselý, V. (2019) Introducing SPAIN (SParse Audio INpainter), 27th European Signal Processing Conference (EUSIPCO), A Coruna, 1-5. doi: 10.23919/EUSIPCO.2019.8902560
  16. Needell, D. (2009) Noisy signal recovery via iterative reweighted L1-minimization, Forty-Third Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 113-117. doi: 10.1109/ACSSC.2009.5470154
  17. Orcalli, A. (2001) On the methodologies of audio restoration, Journal of New Music Research, 30(4), 307-322. doi: 10.1076/jnmr.30.4.307.7496
  18. Ozerov, A., Bilen, Ç. and Pérez, P. (2016) Multichannel audio declipping, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 659-663. doi: 10.1109/ICASSP.2016.7471757
  19. Plumbley, M. D., Blumensath, T., Daudet, L., Gribonval, R. and Davies, M. E. (2010) Sparse representations in audio and music: From coding to source separation, Proceedings of the IEEE, 98(6), 995-1005. doi: 10.1109/JPROC.2009.2030345
  20. Tauböck, G., Rajbamshi, S. and Balazs, P. (2021) Dictionary learning for sparse audio inpainting, IEEE Journal of Selected Topics in Signal Processing, 15(1), 104-119. doi: 10.1109/JSTSP.2020.3046422
  21. Weinstein, A. J. and Wakin, M. B. (2011) Recovering a clipped signal in sparseland. Erişim Adresi: https://arxiv.org/pdf/1110.5063 (Erişim Tarihi: 02.09.2021).
  22. Záviška, P., Rajmic, P., Ozerov, A. and Rencker, L. (2021) A survey and an extensive evaluation of popular audio declipping methods, IEEE Journal of Selected Topics in Signal Processing, 15(1), 5-24. doi: 10.1109/JSTSP.2020.3042071